WeChat Mini Program
Old Version Features

How Interactions Between Temperature and Resources Scale from Populations to Communities

crossref(2024)

University of Connecticut

Cited 0|Views7
Abstract
Temperature and resources are fundamental factors that determine the ability of organisms to function and survive, while influencing their development, growth, and reproduction. Major bodies of ecological theory have emerged, largely independently, to address temperature and resource effects. It remains a major challenge to unite these ideas and determine the interactive effects of temperature and resources on ecological patterns and processes, and their consequences across ecological scales. Here, we propose a simple, physiologically motivated model capturing the interactive effects of temperature and resources (including inorganic nutrients and light) on the growth of microbial ectotherms over multiple ecological scales. From this model we derive a set of key predictions. At the population level, we predict (i) interactive effects of resource limitation on thermal traits, (ii) consistent differences in the temperature sensitivity of auto- and heterotrophs, and (iii) the existence of specific tradeoffs between traits that determine the shape of thermal performance curves. At the community level, we derive predictions for (iv) how limitation by nutrients and light can change the relationship between temperature and productivity. All four predictions are upheld, based on our analyses of a large compilation of laboratory data on microbial growth, as well as field experiments with marine phytoplankton communities. Collectively, our modeling framework provides a new way of thinking about the interplay between two fundamental aspects of life - temperature and resources - and how they constrain and structure ecological properties across scales. Providing links between population and community responses to simultaneous changes in abiotic factors is essential to anticipating the multifaceted effects of global change. ### Competing Interest Statement The authors have declared no competing interest.
More
Translated text
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined